


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		StableDiffusionInstructPixaPixPipeline
			_UpperCamelCase		:       List[Any] 			=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			_UpperCamelCase		:       Optional[Any] 			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCamelCase		:       Tuple 			=		IMAGE_TO_IMAGE_IMAGE_PARAMS
			_UpperCamelCase		:       Union[str, Any] 			=		IMAGE_TO_IMAGE_IMAGE_PARAMS




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							UNetaDConditionModel(
						    block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=8   ,    out_channels=4   ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,    cross_attention_dim=32   ,    )
						UpperCamelCase__       =							PNDMScheduler(skip_prk_steps=snake_case  )
						torch.manual_seed(0  )
						UpperCamelCase__       =							AutoencoderKL(
						    block_out_channels=[32, 64]   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,    latent_channels=4   ,    )
						torch.manual_seed(0  )
						UpperCamelCase__       =							CLIPTextConfig(
						    bos_token_id=0   ,    eos_token_id=2   ,    hidden_size=32   ,    intermediate_size=37   ,    layer_norm_eps=1E-0_5   ,    num_attention_heads=4   ,    num_hidden_layers=5   ,    pad_token_id=1   ,    vocab_size=1000   ,    )
						UpperCamelCase__       =							CLIPTextModel(snake_case  )
						UpperCamelCase__       =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						UpperCamelCase__       =							{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


						UpperCamelCase__       =							floats_tensor((1, 3, 32, 32)   ,    rng=random.Random(snake_case  )  ).to(snake_case  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  )[0]
						UpperCamelCase__       =							Image.fromarray(np.uinta(snake_case  )  ).convert("RGB"  )
						if str(snake_case  ).startswith("mps"  ):
									UpperCamelCase__       =							torch.manual_seed(snake_case  )
						else:
									UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )
						UpperCamelCase__       =							{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "image_guidance_scale": 1,
						    "output_type": "numpy",
						}
						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline(**snake_case  )
						UpperCamelCase__       =							sd_pipe.to(snake_case  )
						sd_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							sd_pipe(**snake_case  ).images
						UpperCamelCase__       =							image[0, -3:, -3:, -1]
						assert image.shape == (1, 32, 32, 3)
						UpperCamelCase__       =							np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline(**snake_case  )
						UpperCamelCase__       =							sd_pipe.to(snake_case  )
						sd_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							"french fries"
						UpperCamelCase__       =							sd_pipe(**snake_case   ,    negative_prompt=snake_case  )
						UpperCamelCase__       =							output.images
						UpperCamelCase__       =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						UpperCamelCase__       =							np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline(**snake_case  )
						UpperCamelCase__       =							sd_pipe.to(snake_case  )
						sd_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							[inputs["prompt"]] * 2

						UpperCamelCase__       =							np.array(inputs["image"]  ).astype(np.floataa  ) / 255.0
						UpperCamelCase__       =							torch.from_numpy(snake_case  ).unsqueeze(0  ).to(snake_case  )
						UpperCamelCase__       =							image / 2 + 0.5
						UpperCamelCase__       =							image.permute(0   ,    3   ,    1   ,    2  )
						UpperCamelCase__       =							image.repeat(2   ,    1   ,    1   ,    1  )

						UpperCamelCase__       =							sd_pipe(**snake_case  ).images
						UpperCamelCase__       =							image[-1, -3:, -3:, -1]

						assert image.shape == (2, 32, 32, 3)
						UpperCamelCase__       =							np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							EulerAncestralDiscreteScheduler(
						    beta_start=0.00085   ,    beta_end=0.012   ,    beta_schedule="scaled_linear"  )
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline(**snake_case  )
						UpperCamelCase__       =							sd_pipe.to(snake_case  )
						sd_pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							sd_pipe(**snake_case  ).images
						UpperCamelCase__       =							image[0, -3:, -3:, -1]

						UpperCamelCase__       =							[round(snake_case   ,    4  ) for x in image_slice.flatten().tolist()]
						print(",".join([str(snake_case  ) for x in slice]  )  )

						assert image.shape == (1, 32, 32, 3)
						UpperCamelCase__       =							np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().test_inference_batch_single_identical(expected_max_diff=3E-3  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline(**snake_case  )
						UpperCamelCase__       =							VaeImageProcessor(do_resize=snake_case   ,    do_normalize=snake_case  )
						UpperCamelCase__       =							pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							pipe(**self.get_dummy_inputs_by_type(snake_case   ,    input_image_type="pt"  )  )[0]

						UpperCamelCase__       =							components["vae"]
						UpperCamelCase__       =							self.get_dummy_inputs_by_type(snake_case   ,    input_image_type="pt"  )

						for image_param in self.image_latents_params:
									if image_param in inputs.keys():
												UpperCamelCase__       =							vae.encode(inputs[image_param]  ).latent_dist.mode()

						UpperCamelCase__       =							pipe(**snake_case  )[0]

						UpperCamelCase__       =							np.abs(out - out_latents_inputs  ).max()
						self.assertLess(snake_case   ,    1E-4   ,    "passing latents as image input generate different result from passing image"  )







@slow
@require_torch_gpu
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




			def 					snake_case__					(				self   ,    snake_case=0  ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.manual_seed(snake_case  )
						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"  )
						UpperCamelCase__       =							{
						    "prompt": "turn him into a cyborg",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 7.5,
						    "image_guidance_scale": 1.0,
						    "output_type": "numpy",
						}
						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"   ,    safety_checker=snake_case  )
						pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing()

						UpperCamelCase__       =							self.get_inputs()
						UpperCamelCase__       =							pipe(**snake_case  ).images
						UpperCamelCase__       =							image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__       =							np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]  )

						assert np.abs(expected_slice - image_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"   ,    safety_checker=snake_case  )
						UpperCamelCase__       =							LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
						pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing()

						UpperCamelCase__       =							self.get_inputs()
						UpperCamelCase__       =							pipe(**snake_case  ).images
						UpperCamelCase__       =							image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__       =							np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]  )

						assert np.abs(expected_slice - image_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"   ,    safety_checker=snake_case  )
						UpperCamelCase__       =							DDIMScheduler.from_config(pipe.scheduler.config  )
						pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing()

						UpperCamelCase__       =							self.get_inputs()
						UpperCamelCase__       =							pipe(**snake_case  ).images
						UpperCamelCase__       =							image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						UpperCamelCase__       =							np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]  )

						assert np.abs(expected_slice - image_slice  ).max() < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							0

						def callback_fn(snake_case   ,    snake_case   ,    snake_case  ) -> None:
									UpperCamelCase__       =							True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
												UpperCamelCase__       =							latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												UpperCamelCase__       =							latents[0, -3:, -3:, -1]
												UpperCamelCase__       =							np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]  )

												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2
									elif step == 2:
												UpperCamelCase__       =							latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												UpperCamelCase__       =							latents[0, -3:, -3:, -1]
												UpperCamelCase__       =							np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]  )

												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2

						UpperCamelCase__       =							False

						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"   ,    safety_checker=snake_case   ,    torch_dtype=torch.floataa  )
						UpperCamelCase__       =							pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing()

						UpperCamelCase__       =							self.get_inputs()
						pipe(**snake_case   ,    callback=snake_case   ,    callback_steps=1  )
						assert callback_fn.has_been_called
						assert number_of_steps == 3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"   ,    safety_checker=snake_case   ,    torch_dtype=torch.floataa  )
						UpperCamelCase__       =							pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing(1  )
						pipe.enable_sequential_cpu_offload()

						UpperCamelCase__       =							self.get_inputs()
						UpperCamelCase__       =							pipe(**snake_case  )

						UpperCamelCase__       =							torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 10**9







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						UpperCamelCase__       =							inputs["image"].resize((504, 504)  )

						UpperCamelCase__       =							"timbrooks/instruct-pix2pix"
						UpperCamelCase__       =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    snake_case   ,    safety_checker=snake_case   ,    )
						pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )
						pipe.enable_attention_slicing()

						UpperCamelCase__       =							pipe(**snake_case  )
						UpperCamelCase__       =							output.images[0]

						UpperCamelCase__       =							image[255:258, 383:386, -1]

						assert image.shape == (504, 504, 3)
						UpperCamelCase__       =							np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3






from unittest.mock import Mock, patch

from file_transfer.send_file import send_file
@patch("socket.socket"	)
@patch("builtins.open"	)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str	)->		int:
			# ===== initialization =====
			UpperCamelCase__       =							Mock()
			UpperCamelCase__       =							conn, Mock()
			UpperCamelCase__       =							iter([1, None]	)
			UpperCamelCase__       =							lambda _A	: next(_A	)

			# ===== invoke =====
			send_file(filename="mytext.txt"					,  testing=_A	)

			# ===== ensurance =====
			sock.assert_called_once()
			sock.return_value.bind.assert_called_once()
			sock.return_value.listen.assert_called_once()
			sock.return_value.accept.assert_called_once()
			conn.recv.assert_called_once()

			file.return_value.__enter__.assert_called_once()
			file.return_value.__enter__.return_value.read.assert_called()

			conn.send.assert_called_once()
			conn.close.assert_called_once()
			sock.return_value.shutdown.assert_called_once()
			sock.return_value.close.assert_called_once()


